{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, you'll learn how to use AutoGen to create a financial AI Agent.\n",
        "\n",
        "\n",
        "* I created two agents: **AssistantAgent** and **UserProxyAgent** (cf below for explanation)\n",
        "\n",
        "* I first asked to fetch historical prices and plot them for NVIDIA.\n",
        "\n",
        "* Then, I asked the agents to propose a momentum trading strategy with a Python code implementation, compute it using NVIDIA's historical prices, and save the results as both a plot and a CSV file.\n"
      ],
      "metadata": {
        "id": "RNbrSX3l-Xpv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Hanane D](https://www.linkedin.com/in/hanane-d-algo-trader)"
      ],
      "metadata": {
        "id": "i4ikdQ5UBEcj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import PIL.Image\n",
        "image_path='./code/nvidia_trading_strategy.png'\n",
        "img = PIL.Image.open(image_path)\n",
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "7BlFwO0d_Ymk",
        "outputId": "2db08697-9f43-42d1-efb4-d70c0abf8ef3"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1400x700>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install and Import Lib"
      ],
      "metadata": {
        "id": "C3b_N6SuaFRz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyautogen -q"
      ],
      "metadata": {
        "id": "NUhCouHZOl9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dask[dataframe] -q"
      ],
      "metadata": {
        "id": "_utrouzEaKTL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Y-xaLn_YM4rF"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from autogen import AssistantAgent, UserProxyAgent, ConversableAgent\n",
        "from autogen.coding import LocalCommandLineCodeExecutor"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')"
      ],
      "metadata": {
        "id": "Ha6RyPOfPB-P"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Agents"
      ],
      "metadata": {
        "id": "yFlIrv81Av95"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initiate the Command Line Code executor"
      ],
      "metadata": {
        "id": "UfrUYwCNaXZr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You'll see a folder created with \"code\" name.\n",
        "\n",
        "* I'm using Google Colab. I'm seeing it in the top left menu ==> folders ==> \"Code\""
      ],
      "metadata": {
        "id": "yTTNKcS5aber"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "code_executor = LocalCommandLineCodeExecutor(\n",
        "    timeout=60,\n",
        "    work_dir=\"code\",\n",
        ")"
      ],
      "metadata": {
        "id": "YzahYXeuPZaF"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LLM Config"
      ],
      "metadata": {
        "id": "MkOeOk_sz7Vy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config_list = [{\"model\": \"gpt-4o\", \"api_key\": OPENAI_API_KEY}]"
      ],
      "metadata": {
        "id": "q-7ykhD3z6ty"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## AssistantAgent & UserProxyAgent"
      ],
      "metadata": {
        "id": "-Z94_VPTar_y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will create AssistantAgent and UserProxyAgent:\n",
        "\n",
        "*   What is the difference between both agents?:\n",
        "\n",
        "\n",
        "\n",
        "**AssistantAgent**:\n",
        "* It is behaving like an AI Assistant by using an LLM under the hood.\n",
        "\n",
        "* It is not requiring human input or code execution.\n",
        "\n",
        "* It is the standard form of an AI assistant: You give it an instruction like summarize a given text or write a Python code, or refine and correct a given Python code.\n",
        "\n",
        "* The behavior is instructed by the system message.\n",
        "\n",
        "\n",
        "**UserProxyAgent**:\n",
        "It's more sophisticated agent. It can:\n",
        "* Interact with human input\n",
        "* Execute Code\n",
        "* Call functions or tools\n",
        "* You can either enable or disable the use of an LLM. If disabled the Agent will execute code. If enabled and no code to execute, it will generate answers using the LLM.\n",
        "\n",
        "\n",
        "✔ ✅ These 2 agents are sublcasses of a more generic class called **ConversableAgent**.\n",
        "\n",
        "This generic class allows agents to converse with each other through the exchange of the messages while retaining the possibility of human intervention (if asked) to finish the required task.\n"
      ],
      "metadata": {
        "id": "zeiJZ9kbaufK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# config_list = [{\"model\": \"gpt-4o\", \"api_key\": OPENAI_API_KEY}]\n",
        "\n",
        "# Create an AssistantAgent\n",
        "assistant = AssistantAgent(name=\"assistant\", llm_config={\"config_list\": config_list})\n",
        "# human_input_mode per default: assistant.human_input_mode ==> 'NEVER'\n",
        "\n",
        "# Create a UserProxyAgent with code execution and no LLM config.\n",
        "code_executor = UserProxyAgent(name=\"code_executor\", code_execution_config={\"executor\": code_executor}, llm_config=False)\n",
        "# human_input_mode per default: code_executor.human_input_mode ==> \"ALWAYS\" ==> You can modify it to \"NEVER\""
      ],
      "metadata": {
        "id": "DB-H7cWgPHdO"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here are the description of both agents:"
      ],
      "metadata": {
        "id": "DaHvL4xPkfIy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "assistant.description"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "wz39mVaNkchi",
        "outputId": "5430ba0e-fb9c-445f-951a-f7340ab46db6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "code_executor.description"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "gLMH-AMujxUj",
        "outputId": "d1b7eac7-9785-462b-8f3d-402cb05e0df5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'An attentive HUMAN user who can answer questions about the task, and can perform tasks such as running Python code or inputting command line commands at a Linux terminal and reporting back the execution results.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "System message in both agents:"
      ],
      "metadata": {
        "id": "G6ZuMbYloXqd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(assistant.system_message)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0HH70TayhqzM",
        "outputId": "ac095321-a324-48e4-eccc-4ae403551bcd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a helpful AI assistant.\n",
            "Solve tasks using your coding and language skills.\n",
            "In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute.\n",
            "    1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.\n",
            "    2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.\n",
            "Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.\n",
            "When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.\n",
            "If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.\n",
            "If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.\n",
            "When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.\n",
            "Reply \"TERMINATE\" in the end when everything is done.\n",
            "    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(code_executor.system_message) #no system message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8N4tfolh1aY",
        "outputId": "91fc13dd-fc6b-47fa-8938-00d1d00d1a3e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## ConversableAgent class : example of how to configure it\n",
        "# code_executor_agent = ConversableAgent(\n",
        "#     name=\"code_executor_agent\",\n",
        "#     llm_config={\"config_list\": config_list},\n",
        "#     code_execution_config={\"executor\": code_executor},\n",
        "#     human_input_mode=\"ALWAYS\",\n",
        "#     default_auto_reply=\n",
        "#     \"Please continue. If everything is done, reply 'TERMINATE'.\",\n",
        "# )"
      ],
      "metadata": {
        "id": "YJ1GwINri_-v"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Start chating: Historical Prices"
      ],
      "metadata": {
        "id": "r0ztiX4Ehst4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What is the current price of NVIDIA?"
      ],
      "metadata": {
        "id": "SQthow0QpbTa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Process**"
      ],
      "metadata": {
        "id": "5KyXj3tIAaYa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We initiate the chat using the **code_executor** agent. It will send the first message `\"\"\"What date is today? What is the current price of NVIDIA?\"\"\"` to the receiver which the **assistant** agent.\n",
        "\n",
        "* We can also add a parameter max_turns to end the conversation after max_turns discussion between both agents."
      ],
      "metadata": {
        "id": "9QcgYx2OqRIB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1.   The code_executor asked the assistant to answer the question `What date is today? What is the current price of NVIDIA?`\n",
        "2.   The assistant gives back the answer to the code_executor using the LLM config and proposed a Python code and a name of the file to save the code in.\n",
        "\n",
        "`Please save the following code in a file and execute it to get the current date and NVIDIA stock price.`\n",
        "\n",
        "`After you execute this script, it will return the current date and the current price of NVIDIA stock. Please run the provided code and share the result.`\n",
        "\n",
        "3.   The code_executor: 1/Saved the file in \"code\" directory  2/ Executed the code 3/Returned the results to the assistant\n",
        "\n",
        "3.   The assistant review the code_executor answer and as the task was completed succefully, it ended the conversation with \"TERMINATE\"\n",
        "\n",
        "`Since all tasks have been completed successfully, this concludes our task.`\n",
        "`TERMINATE`\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "oINW32nUrJew"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chat_result = code_executor.initiate_chat(\n",
        "    assistant,\n",
        "    message=\"\"\"What date is today? What is the current price of NVIDIA?\"\"\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwEii9vrtxSL",
        "outputId": "42c57faa-5d52-49d7-d08c-2002950e1f7e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "code_executor (to assistant):\n",
            "\n",
            "What date is today? What is the current price of NVIDIA?\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "To answer your questions, we need to follow two steps:\n",
            "1. Get the current date.\n",
            "2. Get the current price of NVIDIA stock.\n",
            "\n",
            "### Step 1: Get the current date.\n",
            "We can get the current date using Python.\n",
            "\n",
            "```python\n",
            "from datetime import datetime\n",
            "\n",
            "# Get the current date\n",
            "current_date = datetime.now().strftime(\"%Y-%m-%d\")\n",
            "print(f\"Today's date is: {current_date}\")\n",
            "```\n",
            "\n",
            "### Step 2: Get the current price of NVIDIA.\n",
            "To get the current stock price of NVIDIA, we will use a financial API such as Alpha Vantage or Yahoo Finance. Here's an example using the `yfinance` library.\n",
            "\n",
            "```python\n",
            "# Install yfinance and pandas via pip:\n",
            "# !pip install yfinance pandas\n",
            "\n",
            "import yfinance as yf\n",
            "\n",
            "# Get NVIDIA stock data\n",
            "nvidia = yf.Ticker(\"NVDA\")\n",
            "nvidia_info = nvidia.history(period=\"1d\")\n",
            "\n",
            "# Extract the latest closing price\n",
            "current_price = nvidia_info['Close'].iloc[-1]\n",
            "print(f\"Current price of NVIDIA stock is: ${current_price:.2f}\")\n",
            "```\n",
            "\n",
            "Please save the following code in a file and execute it to get the current date and NVIDIA stock price.\n",
            "```python\n",
            "# filename: date_and_stock_price.py\n",
            "\n",
            "from datetime import datetime\n",
            "import yfinance as yf\n",
            "\n",
            "# Step 1: Get the current date\n",
            "current_date = datetime.now().strftime(\"%Y-%m-%d\")\n",
            "print(f\"Today's date is: {current_date}\")\n",
            "\n",
            "# Step 2: Get NVIDIA stock price\n",
            "nvidia = yf.Ticker(\"NVDA\")\n",
            "nvidia_info = nvidia.history(period=\"1d\")\n",
            "current_price = nvidia_info['Close'].iloc[-1]\n",
            "print(f\"Current price of NVIDIA stock is: ${current_price:.2f}\")\n",
            "```\n",
            "\n",
            "After you execute this script, it will return the current date and the current price of NVIDIA stock. Please run the provided code and share the result.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: \n",
            "\n",
            ">>>>>>>> NO HUMAN INPUT RECEIVED.\n",
            "\n",
            ">>>>>>>> USING AUTO REPLY...\n",
            "\n",
            ">>>>>>>> EXECUTING 3 CODE BLOCKS (inferred languages are [python, python, python])...\n",
            "code_executor (to assistant):\n",
            "\n",
            "exitcode: 0 (execution succeeded)\n",
            "Code output: Today's date is: 2024-09-05\n",
            "Current price of NVIDIA stock is: $106.21\n",
            "Today's date is: 2024-09-05\n",
            "Current price of NVIDIA stock is: $106.21\n",
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "Great! The code executed successfully, and the output indicates:\n",
            "- Today's date is: 2024-09-05\n",
            "- Current price of NVIDIA stock is: $106.21\n",
            "\n",
            "Since all tasks have been completed successfully, this concludes our task.\n",
            "\n",
            "TERMINATE\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: \n",
            "\n",
            ">>>>>>>> NO HUMAN INPUT RECEIVED.\n",
            "\n",
            ">>>>>>>> USING AUTO REPLY...\n",
            "code_executor (to assistant):\n",
            "\n",
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "TERMINATE\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: \n",
            "\n",
            ">>>>>>>> NO HUMAN INPUT RECEIVED.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "###The generated files:\n",
        "\n",
        "# ## ***********FILE 1***********\n",
        "# # Install yfinance and pandas via pip:\n",
        "# # !pip install yfinance pandas\n",
        "\n",
        "# import yfinance as yf\n",
        "\n",
        "# # Get NVIDIA stock data\n",
        "# nvidia = yf.Ticker(\"NVDA\")\n",
        "# nvidia_info = nvidia.history(period=\"1d\")\n",
        "\n",
        "# # Extract the latest closing price\n",
        "# current_price = nvidia_info['Close'].iloc[-1]\n",
        "# print(f\"Current price of NVIDIA stock is: ${current_price:.2f}\")\n",
        "\n",
        "# ## ***********FILE 2***********\n",
        "# from datetime import datetime\n",
        "\n",
        "# # Get the current date\n",
        "# current_date = datetime.now().strftime(\"%Y-%m-%d\")\n",
        "# print(f\"Today's date is: {current_date}\")\n",
        "\n",
        "# ## ***********FILE 3***********\n",
        "# # filename: date_and_stock_price.py\n",
        "\n",
        "# from datetime import datetime\n",
        "# import yfinance as yf\n",
        "\n",
        "# # Step 1: Get the current date\n",
        "# current_date = datetime.now().strftime(\"%Y-%m-%d\")\n",
        "# print(f\"Today's date is: {current_date}\")\n",
        "\n",
        "# # Step 2: Get NVIDIA stock price\n",
        "# nvidia = yf.Ticker(\"NVDA\")\n",
        "# nvidia_info = nvidia.history(period=\"1d\")\n",
        "# current_price = nvidia_info['Close'].iloc[-1]\n",
        "# print(f\"Current price of NVIDIA stock is: ${current_price:.2f}\")"
      ],
      "metadata": {
        "id": "HGZoSnUZs39E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chat_result.chat_history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Spr5waXCuIhb",
        "outputId": "dcfd13a3-51a9-47f2-ad70-ddc84ae9801c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'content': 'What date is today? What is the current price of NVIDIA?',\n",
              "  'role': 'assistant',\n",
              "  'name': 'code_executor'},\n",
              " {'content': 'To answer your questions, we need to follow two steps:\\n1. Get the current date.\\n2. Get the current price of NVIDIA stock.\\n\\n### Step 1: Get the current date.\\nWe can get the current date using Python.\\n\\n```python\\nfrom datetime import datetime\\n\\n# Get the current date\\ncurrent_date = datetime.now().strftime(\"%Y-%m-%d\")\\nprint(f\"Today\\'s date is: {current_date}\")\\n```\\n\\n### Step 2: Get the current price of NVIDIA.\\nTo get the current stock price of NVIDIA, we will use a financial API such as Alpha Vantage or Yahoo Finance. Here\\'s an example using the `yfinance` library.\\n\\n```python\\n# Install yfinance and pandas via pip:\\n# !pip install yfinance pandas\\n\\nimport yfinance as yf\\n\\n# Get NVIDIA stock data\\nnvidia = yf.Ticker(\"NVDA\")\\nnvidia_info = nvidia.history(period=\"1d\")\\n\\n# Extract the latest closing price\\ncurrent_price = nvidia_info[\\'Close\\'].iloc[-1]\\nprint(f\"Current price of NVIDIA stock is: ${current_price:.2f}\")\\n```\\n\\nPlease save the following code in a file and execute it to get the current date and NVIDIA stock price.\\n```python\\n# filename: date_and_stock_price.py\\n\\nfrom datetime import datetime\\nimport yfinance as yf\\n\\n# Step 1: Get the current date\\ncurrent_date = datetime.now().strftime(\"%Y-%m-%d\")\\nprint(f\"Today\\'s date is: {current_date}\")\\n\\n# Step 2: Get NVIDIA stock price\\nnvidia = yf.Ticker(\"NVDA\")\\nnvidia_info = nvidia.history(period=\"1d\")\\ncurrent_price = nvidia_info[\\'Close\\'].iloc[-1]\\nprint(f\"Current price of NVIDIA stock is: ${current_price:.2f}\")\\n```\\n\\nAfter you execute this script, it will return the current date and the current price of NVIDIA stock. Please run the provided code and share the result.',\n",
              "  'role': 'user',\n",
              "  'name': 'assistant'},\n",
              " {'content': \"exitcode: 0 (execution succeeded)\\nCode output: Today's date is: 2024-09-05\\nCurrent price of NVIDIA stock is: $106.21\\nToday's date is: 2024-09-05\\nCurrent price of NVIDIA stock is: $106.21\\n\",\n",
              "  'role': 'assistant',\n",
              "  'name': 'code_executor'},\n",
              " {'content': \"Great! The code executed successfully, and the output indicates:\\n- Today's date is: 2024-09-05\\n- Current price of NVIDIA stock is: $106.21\\n\\nSince all tasks have been completed successfully, this concludes our task.\\n\\nTERMINATE\",\n",
              "  'role': 'user',\n",
              "  'name': 'assistant'},\n",
              " {'content': '', 'role': 'assistant', 'name': 'code_executor'},\n",
              " {'content': 'TERMINATE', 'role': 'user', 'name': 'assistant'}]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chat_result.cost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjjiSaF6u_sK",
        "outputId": "5a2b24e9-2759-445f-fff0-dfbeefe07592"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'usage_including_cached_inference': {'total_cost': 0.03834,\n",
              "  'gpt-4o-2024-05-13': {'cost': 0.03834,\n",
              "   'prompt_tokens': 4914,\n",
              "   'completion_tokens': 918,\n",
              "   'total_tokens': 5832}},\n",
              " 'usage_excluding_cached_inference': {'total_cost': 0.005155,\n",
              "  'gpt-4o-2024-05-13': {'cost': 0.005155,\n",
              "   'prompt_tokens': 1022,\n",
              "   'completion_tokens': 3,\n",
              "   'total_tokens': 1025}}}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot the NVIDIA historical price since the beginning of the current year"
      ],
      "metadata": {
        "id": "nHTBuFLpygj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# the assistant receives a message from the user, which contains the task description\n",
        "chat_result = code_executor.initiate_chat(\n",
        "    assistant,\n",
        "    message=\"\"\"Plot the NVIDIA historical price since the beginning of the current year and save the figure to a file called 'nvidia_price.png'\"\"\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzWctkcryf5t",
        "outputId": "dae5a2e6-62fb-4737-82b2-36f533db96f7"
      },
      "execution_count": 55,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "code_executor (to assistant):\n",
            "\n",
            "Plot the NVIDIA historical price since the beginning of the current year and save the figure to a file called 'nvidia_price.png'\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "To plot the NVIDIA historical price since the beginning of the current year and save the figure to a file, we can use Python with the `yfinance` and `matplotlib` libraries. Here is the plan:\n",
            "\n",
            "1. Install necessary libraries (`yfinance` and `matplotlib`).\n",
            "2. Fetch the historical price data for NVIDIA (ticker: \"NVDA\") since the beginning of the current year using the `yfinance` library.\n",
            "3. Plot the data using `matplotlib`.\n",
            "4. Save the resulting plot to a file called 'nvidia_price.png'.\n",
            "\n",
            "Let's proceed with the code:\n",
            "\n",
            "```python\n",
            "# filename: plot_nvidia_price.py\n",
            "\n",
            "import yfinance as yf\n",
            "import matplotlib.pyplot as plt\n",
            "from datetime import datetime\n",
            "\n",
            "# Fetch NVIDIA historical price data since beginning of the current year\n",
            "ticker = 'NVDA'\n",
            "start_date = datetime(datetime.now().year, 1, 1).strftime('%Y-%m-%d')\n",
            "data = yf.download(ticker, start=start_date)\n",
            "\n",
            "# Plot the historical price data\n",
            "plt.figure(figsize=(10, 5))\n",
            "plt.plot(data.index, data['Close'], label='NVIDIA Closing Price')\n",
            "plt.title('NVIDIA Historical Price Since Beginning of Current Year')\n",
            "plt.xlabel('Date')\n",
            "plt.ylabel('Close Price (USD)')\n",
            "plt.legend()\n",
            "plt.grid(True)\n",
            "plt.savefig('nvidia_price.png')\n",
            "print(\"The figure has been saved as 'nvidia_price.png'\")\n",
            "```\n",
            "\n",
            "Please save this code to a file named `plot_nvidia_price.py` and then execute it. This code will fetch the historical prices for NVIDIA, plot the closing prices, and save the plot to 'nvidia_price.png'.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: \n",
            "\n",
            ">>>>>>>> NO HUMAN INPUT RECEIVED.\n",
            "\n",
            ">>>>>>>> USING AUTO REPLY...\n",
            "\n",
            ">>>>>>>> EXECUTING CODE BLOCK (inferred language is python)...\n",
            "code_executor (to assistant):\n",
            "\n",
            "exitcode: 0 (execution succeeded)\n",
            "Code output: \n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "The figure has been saved as 'nvidia_price.png'\n",
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "The code executed successfully, and the figure has been saved as 'nvidia_price.png'.\n",
            "\n",
            "Everything is complete now.\n",
            "\n",
            "TERMINATE\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: \n",
            "\n",
            ">>>>>>>> NO HUMAN INPUT RECEIVED.\n",
            "\n",
            ">>>>>>>> USING AUTO REPLY...\n",
            "code_executor (to assistant):\n",
            "\n",
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "TERMINATE\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: exit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# chat_result.chat_history\n",
        "chat_result.cost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWMhxbgnzuYS",
        "outputId": "c6b2d83c-7c4f-4a43-ad49-029224d78106"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'usage_including_cached_inference': {'total_cost': 0.08425,\n",
              "  'gpt-4o-2024-05-13': {'cost': 0.08425,\n",
              "   'prompt_tokens': 11522,\n",
              "   'completion_tokens': 1776,\n",
              "   'total_tokens': 13298}},\n",
              " 'usage_excluding_cached_inference': {'total_cost': 0.051065,\n",
              "  'gpt-4o-2024-05-13': {'cost': 0.051065,\n",
              "   'prompt_tokens': 7630,\n",
              "   'completion_tokens': 861,\n",
              "   'total_tokens': 8491}}}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Ct69wE4EzuLq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "4FXvhFeOyfns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import PIL.Image\n",
        "# #local image\n",
        "image_path='./code/nvidia_price.png'\n",
        "img = PIL.Image.open(image_path)\n",
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "2RSerinhzY-K",
        "outputId": "7266b2fe-3030-41f7-d399-ff74beccebe5"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1000x500>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can modify LLM config to lower your cost:"
      ],
      "metadata": {
        "id": "DMws1kkA0FKZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# config_list = [{\"model\": \"gpt-4o-mini\", \"api_key\": OPENAI_API_KEY}]\n",
        "# assistant_4o_mini = AssistantAgent(name=\"assistant\", llm_config={\"config_list\": config_list})\n",
        "\n",
        "# # You can modify code_executor.human_input_mode to \"NEVER\""
      ],
      "metadata": {
        "id": "dymD8em40DJx"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Start Chating: Momentum Trading Strategy"
      ],
      "metadata": {
        "id": "LmAvjMUU-CWd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Propose a Momentum Trading Strategy and Plot it for NVIDIA"
      ],
      "metadata": {
        "id": "lMkzJLGS-MG9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chat_result = code_executor.initiate_chat(\n",
        "    assistant,\n",
        "    message=\"\"\"Let's proceed step by step:\n",
        "    1- Propose a Python code implementation of one momentum trading strategy\n",
        "    2- Save in a file called 'momentum_trading_strategy.py'\n",
        "    2- Apply this code to NVIDIA historical price of the current year.\n",
        "    4- Save the results in a csv file called 'nvidia_trading_strategy.csv'\n",
        "    5- Plot this trading strategy and save it in a file called 'nvidia_trading_strategy.png'\"\"\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xy6JHYEd0g2h",
        "outputId": "ada7834d-8f6c-4c91-aad6-c357bec9b1db"
      },
      "execution_count": 66,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "code_executor (to assistant):\n",
            "\n",
            "Let's proceed step by step:\n",
            "    1- Propose a Python code implementation of one momentum trading strategy\n",
            "    2- Save in a file called 'momentum_trading_strategy.py'\n",
            "    2- Apply this code to NVIDIA historical price of the current year.\n",
            "    4- Save the results in a csv file called 'nvidia_trading_strategy.csv'\n",
            "    5- Plot this trading strategy and save it in a file called 'nvidia_trading_strategy.png'\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "Sure, let's proceed step-by-step as you requested.\n",
            "\n",
            "### Step 1: Implement a Momentum Trading Strategy\n",
            "A simple momentum trading strategy could be based on comparing the short-term moving average (e.g., 20 days) with a long-term moving average (e.g., 50 days). When the short-term moving average crosses above the long-term moving average, it signals a buy. Conversely, when the short-term moving average crosses below the long-term moving average, it signals a sell.\n",
            "\n",
            "### Step 2: Save the Code in a File Called `momentum_trading_strategy.py`\n",
            "We'll implement and save the code in the specified file.\n",
            "\n",
            "```python\n",
            "# filename: momentum_trading_strategy.py\n",
            "\n",
            "import pandas as pd\n",
            "import yfinance as yf\n",
            "import matplotlib.pyplot as plt\n",
            "\n",
            "def fetch_data(ticker, start_date, end_date):\n",
            "    data = yf.download(ticker, start=start_date, end=end_date)\n",
            "    return data\n",
            "\n",
            "def calculate_moving_averages(data, short_window, long_window):\n",
            "    data['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1).mean()\n",
            "    data['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1).mean()\n",
            "    return data\n",
            "\n",
            "def generate_signals(data):\n",
            "    data['Signal'] = 0\n",
            "    data['Signal'][short_window:] = \\\n",
            "        np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1.0, 0.0)\n",
            "    data['Position'] = data['Signal'].diff()\n",
            "    return data\n",
            "\n",
            "def plot_trading_strategy(data, plot_file):\n",
            "    # Plot the closing price and the moving averages\n",
            "    plt.figure(figsize=(14, 7))\n",
            "    plt.plot(data['Close'], label='Close Price')\n",
            "    plt.plot(data['Short_MA'], label=f'{short_window}-Day MA')\n",
            "    plt.plot(data['Long_MA'], label=f'{long_window}-Day MA')\n",
            "\n",
            "    # Plot the buy signals\n",
            "    plt.plot(data[data['Position'] == 1].index,\n",
            "             data['Short_MA'][data['Position'] == 1],\n",
            "             '^', markersize=10, color='g', lw=0, label='Buy Signal')\n",
            "\n",
            "    # Plot the sell signals\n",
            "    plt.plot(data[data['Position'] == -1].index,\n",
            "             data['Long_MA'][data['Position'] == -1],\n",
            "             'v', markersize=10, color='r', lw=0, label='Sell Signal')\n",
            "\n",
            "    plt.title('Momentum Trading Strategy')\n",
            "    plt.xlabel('Date')\n",
            "    plt.ylabel('Price')\n",
            "    plt.legend()\n",
            "    plt.grid()\n",
            "    plt.savefig(plot_file)\n",
            "    plt.show()\n",
            "\n",
            "if __name__ == \"__main__\":\n",
            "    import numpy as np\n",
            "\n",
            "    ticker = 'NVDA'\n",
            "    start_date = '2023-01-01'\n",
            "    end_date = pd.Timestamp.today().strftime('%Y-%m-%d')\n",
            "    short_window = 20\n",
            "    long_window = 50\n",
            "    data_file = 'nvidia_trading_strategy.csv'\n",
            "    plot_file = 'nvidia_trading_strategy.png'\n",
            "\n",
            "    data = fetch_data(ticker, start_date, end_date)\n",
            "    data = calculate_moving_averages(data, short_window, long_window)\n",
            "    data = generate_signals(data)\n",
            "\n",
            "    # Save the data with signals into a csv file\n",
            "    data.to_csv(data_file)\n",
            "\n",
            "    # Plot the trading strategy\n",
            "    plot_trading_strategy(data, plot_file)\n",
            "\n",
            "    print(f\"Trading strategy saved in {data_file}\")\n",
            "    print(f\"Plot saved in {plot_file}\")\n",
            "```\n",
            "\n",
            "### Instructions to Execute\n",
            "1. Save the above Python code in a file named `momentum_trading_strategy.py`.\n",
            "2. Navigate to the directory where `momentum_trading_strategy.py` is saved.\n",
            "3. Run the script using Python:\n",
            "   ```sh\n",
            "   python momentum_trading_strategy.py\n",
            "   ```\n",
            "\n",
            "### Step 3 & 4: Apply to NVIDIA Historical Data and Save Results\n",
            "The script above fetches NVIDIA historical data from the start of the current year to today. The data including the trading signals will be saved to a CSV file named `nvidia_trading_strategy.csv`.\n",
            "\n",
            "### Step 5: Plot the Trading Strategy and Save the Plot\n",
            "The script will also generate and save a plot of the trading strategy to a file named `nvidia_trading_strategy.png`.\n",
            "\n",
            "Please proceed with saving the code to `momentum_trading_strategy.py` and running it. Let me know if you encounter any issues!\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: \n",
            "\n",
            ">>>>>>>> NO HUMAN INPUT RECEIVED.\n",
            "\n",
            ">>>>>>>> USING AUTO REPLY...\n",
            "\n",
            ">>>>>>>> EXECUTING 2 CODE BLOCKS (inferred languages are [python, sh])...\n",
            "code_executor (to assistant):\n",
            "\n",
            "exitcode: 0 (execution succeeded)\n",
            "Code output: \n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "/content/code/momentum_trading_strategy.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['Signal'][short_window:] = \\\n",
            "Figure(1400x700)\n",
            "Trading strategy saved in nvidia_trading_strategy.csv\n",
            "Plot saved in nvidia_trading_strategy.png\n",
            "\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "/content/code/momentum_trading_strategy.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['Signal'][short_window:] = \\\n",
            "Figure(1400x700)\n",
            "Trading strategy saved in nvidia_trading_strategy.csv\n",
            "Plot saved in nvidia_trading_strategy.png\n",
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "The script executed successfully, and the output files were generated as expected:\n",
            "\n",
            "- `nvidia_trading_strategy.csv` (contains the historical price data along with the strategy signals)\n",
            "- `nvidia_trading_strategy.png` (contains the plot of the momentum trading strategy)\n",
            "\n",
            "There was a `SettingWithCopyWarning` indicating that there is an operation on a copy of a slice from a DataFrame which is not directly problematic but can be avoided for cleaner code.\n",
            "\n",
            "To address this warning, we can modify the code to avoid chained assignment, ensuring that the operations are performed on the original DataFrame or properly setting the values without warning.\n",
            "\n",
            "### Here's the updated code to address the warning:\n",
            "\n",
            "```python\n",
            "# filename: momentum_trading_strategy.py\n",
            "\n",
            "import pandas as pd\n",
            "import yfinance as yf\n",
            "import matplotlib.pyplot as plt\n",
            "import numpy as np\n",
            "\n",
            "def fetch_data(ticker, start_date, end_date):\n",
            "    data = yf.download(ticker, start=start_date, end=end_date)\n",
            "    return data\n",
            "\n",
            "def calculate_moving_averages(data, short_window, long_window):\n",
            "    data['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1).mean()\n",
            "    data['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1).mean()\n",
            "    return data\n",
            "\n",
            "def generate_signals(data, short_window):\n",
            "    data['Signal'] = 0\n",
            "    data.loc[data.index[short_window:], 'Signal'] = \\\n",
            "        np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1.0, 0.0)\n",
            "    data['Position'] = data['Signal'].diff()\n",
            "    return data\n",
            "\n",
            "def plot_trading_strategy(data, plot_file):\n",
            "    # Plot the closing price and the moving averages\n",
            "    plt.figure(figsize=(14, 7))\n",
            "    plt.plot(data['Close'], label='Close Price')\n",
            "    plt.plot(data['Short_MA'], label=f'{short_window}-Day MA')\n",
            "    plt.plot(data['Long_MA'], label=f'{long_window}-Day MA')\n",
            "\n",
            "    # Plot the buy signals\n",
            "    plt.plot(data[data['Position'] == 1].index,\n",
            "             data['Short_MA'][data['Position'] == 1],\n",
            "             '^', markersize=10, color='g', lw=0, label='Buy Signal')\n",
            "\n",
            "    # Plot the sell signals\n",
            "    plt.plot(data[data['Position'] == -1].index,\n",
            "             data['Long_MA'][data['Position'] == -1],\n",
            "             'v', markersize=10, color='r', lw=0, label='Sell Signal')\n",
            "\n",
            "    plt.title('Momentum Trading Strategy')\n",
            "    plt.xlabel('Date')\n",
            "    plt.ylabel('Price')\n",
            "    plt.legend()\n",
            "    plt.grid()\n",
            "    plt.savefig(plot_file)\n",
            "    plt.show()\n",
            "\n",
            "if __name__ == \"__main__\":\n",
            "    ticker = 'NVDA'\n",
            "    start_date = '2023-01-01'\n",
            "    end_date = pd.Timestamp.today().strftime('%Y-%m-%d')\n",
            "    short_window = 20\n",
            "    long_window = 50\n",
            "    data_file = 'nvidia_trading_strategy.csv'\n",
            "    plot_file = 'nvidia_trading_strategy.png'\n",
            "\n",
            "    data = fetch_data(ticker, start_date, end_date)\n",
            "    data = calculate_moving_averages(data, short_window, long_window)\n",
            "    data = generate_signals(data, short_window)\n",
            "\n",
            "    # Save the data with signals into a csv file\n",
            "    data.to_csv(data_file)\n",
            "\n",
            "    # Plot the trading strategy\n",
            "    plot_trading_strategy(data, plot_file)\n",
            "\n",
            "    print(f\"Trading strategy saved in {data_file}\")\n",
            "    print(f\"Plot saved in {plot_file}\")\n",
            "```\n",
            "\n",
            "Please save and execute this updated script to eliminate the `SettingWithCopyWarning` warning messages. Let me know if there are any further issues or if you need additional assistance!\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: \n",
            "\n",
            ">>>>>>>> NO HUMAN INPUT RECEIVED.\n",
            "\n",
            ">>>>>>>> USING AUTO REPLY...\n",
            "\n",
            ">>>>>>>> EXECUTING CODE BLOCK (inferred language is python)...\n",
            "code_executor (to assistant):\n",
            "\n",
            "exitcode: 0 (execution succeeded)\n",
            "Code output: \n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Figure(1400x700)\n",
            "Trading strategy saved in nvidia_trading_strategy.csv\n",
            "Plot saved in nvidia_trading_strategy.png\n",
            "\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "assistant (to code_executor):\n",
            "\n",
            "Great! The script executed successfully, and the warning messages have been resolved. The following files have been created:\n",
            "\n",
            "- `nvidia_trading_strategy.csv`: Contains the historical price data along with the trading strategy signals.\n",
            "- `nvidia_trading_strategy.png`: Contains the plot of the momentum trading strategy.\n",
            "\n",
            "Your task is now complete. If you have any more tasks or need further assistance, feel free to ask. \n",
            "\n",
            "TERMINATE\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Replying as code_executor. Provide feedback to assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: exit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the \"code\" directory, you will see the new files generated:\n",
        "* momentum_trading_strategy.py\n",
        "* nvidia_trading_strategy.csv\n",
        "* nvidia_trading_strategy.png"
      ],
      "metadata": {
        "id": "rghpbcoX8t4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chat_result.cost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1eqVO_E94NO",
        "outputId": "1b4ed925-2709-4fa3-d81a-bb4406766c4c"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'usage_including_cached_inference': {'total_cost': 0.136815,\n",
              "  'gpt-4o-2024-05-13': {'cost': 0.136815,\n",
              "   'prompt_tokens': 16533,\n",
              "   'completion_tokens': 3610,\n",
              "   'total_tokens': 20143}},\n",
              " 'usage_excluding_cached_inference': {'total_cost': 0.10363,\n",
              "  'gpt-4o-2024-05-13': {'cost': 0.10363,\n",
              "   'prompt_tokens': 12641,\n",
              "   'completion_tokens': 2695,\n",
              "   'total_tokens': 15336}}}"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot Momentum Trading Strategy"
      ],
      "metadata": {
        "id": "5g4jMs6I9CYP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's have a look on the plot generated about the trading strategy applied to NVIDIA:"
      ],
      "metadata": {
        "id": "UYsdIwZY0cPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import PIL.Image\n",
        "# #local image\n",
        "image_path='./code/nvidia_trading_strategy.png'\n",
        "img = PIL.Image.open(image_path)\n",
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "LCsefzgS75YY",
        "outputId": "48abf5f9-b46f-4a25-d7b8-9c25c2896fd5"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1400x700>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualize Momentum Trading Strategy DataFrame"
      ],
      "metadata": {
        "id": "4BvJyXin9J3v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's have a look on a CSV file in which we have the results of the trading strategy alongside the historical prices of NVIDIA"
      ],
      "metadata": {
        "id": "3jhwNDcA8X0w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "file_path='./code/nvidia_trading_strategy.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9M6g2y-x8AGw",
        "outputId": "f8e0f8b5-78a6-4ed7-db5c-655124f22865"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date    Open    High     Low   Close  Adj Close     Volume  Short_MA  \\\n",
              "0  2023-01-03  14.851  14.996  14.096  14.315  14.306805  401277000   14.3150   \n",
              "1  2023-01-04  14.567  14.853  14.241  14.749  14.740557  431324000   14.5320   \n",
              "2  2023-01-05  14.491  14.564  14.148  14.265  14.256834  389168000   14.4430   \n",
              "3  2023-01-06  14.474  15.010  14.034  14.859  14.850493  405044000   14.5470   \n",
              "4  2023-01-09  15.284  16.056  15.141  15.628  15.619053  504231000   14.7632   \n",
              "\n",
              "   Long_MA  Signal  Position  \n",
              "0  14.3150       0       NaN  \n",
              "1  14.5320       0       0.0  \n",
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          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    }
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}